Translation and validation of the Canadian diabetes risk assessment questionnaire in China
Bibliographic record
Abstract
OBJECTIVES: To adapt the Canadian Diabetes Risk Assessment Questionnaire for the Chinese population and to evaluate its psychometric properties. DESIGN AND SAMPLE: A cross-sectional study was conducted with a convenience sample of 194 individuals aged 35-74 years from October 2014 to April 2015. METHODS: The Canadian Diabetes Risk Assessment Questionnaire was adapted and translated for the Chinese population. Test-retest reliability was conducted to measure stability. Criterion and convergent validity of the adapted questionnaire were assessed using 2-hr 75 g oral glucose tolerance tests and the Finnish Diabetes Risk Scores, respectively. Sensitivity and specificity were evaluated to establish its predictive validity. RESULTS: The test-retest reliability was 0.988. Adequate validity of the adapted questionnaire was demonstrated by positive correlations found between the scores and 2-hr 75 g oral glucose tolerance tests (r = .343, p < .001) and with the Finnish Diabetes Risk Scores (r = .738, p < .001). The area under receiver operating characteristic curve was 0.705 (95% CI .632, .778), demonstrating moderate diagnostic value at a cutoff score of 30. The sensitivity was 73%, with a positive predictive value of 57% and negative predictive value of 78%. CONCLUSIONS: Our results provided evidence supporting the translation consistency, content validity, convergent validity, criterion validity, sensitivity, and specificity of the translated Canadian Diabetes Risk Assessment Questionnaire with minor modifications. This paper provides clinical, practical, and methodological information on how to adapt a diabetes risk calculator between cultures for public health nurses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".